7 ms·

This is exactly the direction I expect AI-assisted coding to go in. Not software engineers being kicked out and some business person pressing a few buttons to h

by paxys 1y ago

This is exactly the direction I expect AI-assisted coding to go in. Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X), but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. The million dollar (perhaps literally) question is – could @kentonv have written this library quicker by himself without any AI help?

dkdcio 1y ago
> The million dollar (perhaps literally) question is – could @kentonv have written this library quicker by himself without any AI help? I *think* the answer to this is clearly no: or at least, given what we can accomplish today with the tools we have now, and that we are still collectively learning how to effectively use this, there's no way it won't be faster (with effective use) in another 3-6 months to fully-code new solutions with AI. I think it requires a lot of work: well-documented, well-structured codebases with fast built-in feedback loops (good linting/unit tests etc.), but we're heading there no
- necovek 1y ago
  In a "well-documented, well-structured codebase with fast built-in feedback loops", a human programmer is really empowered to make changes fast. This is exactly what's needed for fast iteration, including in unfamiliar codebases. When you are not introducing a new pattern in the code structure, it's mostly copy-paste and then edit. But it's also extremely rare, so a pretty high bar to be able to benefit from tools like AI.
- motorest 1y ago
  > I think the answer to this is clearly no: or at least, given what we can accomplish today with the tools we have now, and that we are still collectively learning how to effectively use this, there's no way it won't be faster (with effective use) in another 3-6 months to fully-code new solutions with AI. I think these discussions need to start from another point. The techniques changed radically, and so did the way problems are tackled. It's not that a software engineer is/was unable to deliver a project with/without LLMs. That's a red herring. The key aspects are things like the overall quality of the work being delivered vs how much time it took to reach that level of quality. For example, one of the primary ways a LLM is used is not to write code at all: it's to explain to you what you are looking at. Whether it's used as a Google substitute or a rubber duck, developers are able to reason with existing projects and even explore approaches and strategies to tackle problem like they were never able to do so. You no longer need to book meetings with a principal engineer to as questions: you just drop a line in Copilot Chat and ask away. Another critical aspect is that LLMs help you explore options faster, and iterate over them. This allows you to figure out what approach works best for your scenario and adapt to emerging requirements without having to even chat with anyone. This means that, within the timeframe you would deliver the first iteration of a MVP, you can very easily deliver a much more stable project.
  - colonCapitalDee 1y ago
    I'm had great success with downloading source code and docs and using Claude Code to query them
  - james_marks 1y ago
    Exactly this > Another critical aspect is that LLMs help you explore options faster, and iterate over them. This allows you to figure out what approach works best for your scenario and adapt to emerging requirements without having to even chat with anyone. This means that, within the timeframe you would deliver the first iteration of a MVP, you can very easily deliver a much more stable project
bigstrat2003 1y ago
> but rather experienced engineers using AI to generate bits of code and then meticulously testing and reviewing them. My problem is that (in my experience anyways) this is slower than me just writing the code myself. That's why AI is not a useful tool right now. They only get it right sometimes so it winds up being easier to just do it yourself in the first place. As the saying goes: bad help is worse than no help at all, and AI is bad help right now.
- uludag 1y ago
  I feel this is on point. So not only is there the time lost correcting and testing AI generated code, but there's also the mental model you build of the code when you write it yourself. Assuming you want a strong mental model of what the code does and how it works (which you'd use in conversations with stakeholders and architecture discussions for example), writing the code manually, with perhaps minor completion-like AI assistance, may be the optimal approach.
- JimDabell 1y ago
  > My problem is that (in my experience anyways) this is slower than me just writing the code myself. How much experience do you have writing code vs how much experience do you have prompting using AI though? You have to factor in that these tools are new and everybody is still figuring out how to use them effectively.
  - imiric 1y ago
    > You have to factor in that these tools are new and everybody is still figuring out how to use them effectively. I think that the skills required are highly overblown. The user should be aware of what each model excels at, its context size, temperature, and other parameters; how to communicate well, set system prompts and phrase tasks in a clear, succinct yet informative way; how to refocus the session when it veers off track; keep up to date with the latest (<~6mo) concepts and tooling, and so on. All of this is trivial for a competent software engineer. The idea that it requires some specialized training that couldn't be attained by experimentation and reading a blog post is absurd. "Prompt engineering" just isn't a thing.
belter 1y ago
The million-dollar question is not whether you can review at the speed the model is coding. It is whether you can trust review alone to catch everything. If a robot assembles cars at lightning speed... but occasionally misaligns a bolt, and your only safeguard is a visual inspection afterward, some defects will roll off the assembly line. Human coders prevent many bugs by thinking during assembly.
- chrisweekly 1y ago
  THIS. IMHO more rigorous test automation (including fuzzing and related techniques) is needed. Actually that holds whether AI is involved or not, but probably more so if it is.
- pton_xd 1y ago
  > Human coders prevent many bugs by thinking during assembly. I'm far from an AI true believer but come on -- human coders write bugs, tons and tons of bugs. According to Peopleware, software has "an average defect density of one to three defects per hundred lines of code"!
  - belter 1y ago
    My point is that the bugs generated by LLM or human coders are different.
- Shorn 1y ago
  And yet, doors still fall off airplanes without any AI in sight.
gokhan 1y ago
> Not software engineers being kicked out ... but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. But what if you only need 2 kentonv's instead of 20 at the end? Do you assume we'll find enough new tasks that will occupy the other 18? I think that's the question. And the author is implementing a fairly technical project in this case. How about routine LoB app development?
- paxys 1y ago
  Increased productivity means increased opportuntity. There isn't going to be a time (at least not anytime soon) when we can all sit back and say "yup, we have accomplished everything there is to do with software and don't need more engineers".
  - spiderice 1y ago
    But there very well might be a time very soon where human's no longer offer economic value to the software engineering process. If you could (and currently you can't) pay an AI $10k/year to do what a human could do in a year, why would you pay the human 6 figures? Or even $20k? Nobody is claiming that human's won't have jobs simply because "we have accomplished everything this is to do". It's that humans will offer zero economic value compared to AI because AI gets so good and so cheap.
    - paxys 1y ago
      And there might be a giant asteroid that strikes the earth a few years down the line ending human civilization. If there is some magic $10k AI that can fully replace a $200k software engineer then I'd love to see it. Until that happens this entire discussion is science fiction.
      - spiderice 1y ago
        If experts were saying the astroid will hit earth in the next 5 years, would it still be science fiction? You acting like those two scenarios are the same is disingenuous. Fuck that.
danans 1y ago
> Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X) The theory of enshittification says that "business person pressing a few buttons" approach will be pursued, even if it lowers quality, to save costs, at least until that approach undermines quality so much that it undermines the business model. However, nobody knows how much quality tradeoff tolerance is there to mine.
stackskipton 1y ago
>experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them And where are supposed to get experienced engineers if replaced all Jr Devs with AI? There is a ton of benefit from drudgery of writing classes even if seems like grunt work at the time.
hooverd 1y ago
AI is great for undifferentiated heavy lifting and surfacing knowledge, but by the time I've made all the decisions, I can just write the code that matters myself there.
kentonv 1y ago
It took me a few days to build the library with AI. I estimate it would have taken a few weeks, maybe months to write by hand. That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec. In my attempts to make changes to the Workers Runtime itself using AI, I've generally not felt like it saved much time. Though, people who don't know the codebase as well as I do have reported it helped them a lot. I have found AI incredibly useful when I jump into other people's complex codebases, that I'm not familiar with. I now feel like I'm comfortable doing that, since AI can help me find my way around very quickly, whereas previously I generally shied away from jumping in and would instead try to get someone on the team to make whatever change I needed.
- philipwhiuk 1y ago
  > Though, people who don't know the codebase as well as I do have reported it helped them a lot. My problem I guess is that maybe this is just Dunning-Kruger esq. When you don't know what you don't know you get the impression it's smart. When you do, you think it's rubbish. Like when you see a media report on a subject you know about and you see it's inaccurate but then somehow still trust the media on a subject you're a non-expert on.
  - throwaway314155 1y ago
    I think most of this just amounts to the same old good developers vs. bad developers situation that we've been in for decades.
  - giantrobot 1y ago
    > Like when you see a media report on a subject you know about and you see it's inaccurate but then somehow still trust the media on a subject you're a non-expert on. Gell-Mann Amnesia https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect
  - motorest 1y ago
    > My problem I guess is that maybe this is just Dunning-Kruger esq. When you don't know what you don't know you get the impression it's smart. When you do, you think it's rubbish. I see your point. Indeed there are two completely different points of view regarding the output of LLMs: * Hey, I managed to vibecode my way into a fully working web service with a React SPA after a couple of prompts, and a full automated test suite to boot. * This project is nowhere as clean as I would have written it, and doesn't even follow my pet coding conventions. One side lauds LLMs, the other complains they output mainly crap. The truth of the matter is that the vast majority of software engineers write crap code, as the definition of "crap code" is "something I would have done differently". Opinionated engineers look at the output of LLMs and accuse it of being crap code. Eppur si muove.
deleted 1y ago
[deleted]
tkiolp4 1y ago
Why is speed important in this context? If the code is published one week/month later, would that affect what exactly? It’s open source.
- kentonv 1y ago
  As it happens, if this were released a month later, it would have been a huge loss for us. This OAuth library is a core component of the Workers Remote MCP framework, which we managed to ship the day before the Remote MCP standard dropped. And because we were there and ready for customers right at the beginning, a whole lot of people ended up building their MCP servers on us, including some big names: https://blog.cloudflare.com/mcp-demo-day/ https://blog.cloudflare.com/mcp-demo-day/ (Also if I had spent a month on this instead of a few days, that would be a month I wasn't spending on other things, and I have kind of a lot to do...)
motorest 1y ago
> This is exactly the direction I expect AI-assisted coding to go in. Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X), but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. There is a middle ground: software engineers being kicked out because now some business person can hand over the task of building the entire OAuth infrastructure to a single inexperienced developer with a Claude account.
- petersellers 1y ago
  I'm not so sure that would work well in practice. How would the inexperienced developer know that the code created by the AI was correct? What if subtle bugs are introduced that the inexperienced developer didn't catch until it went out into production? What if the developer didn't even know how to debug those problems correctly? Would they know that the code they are writing is maintainable and extensible, or are they just going to generate a new layer of code on top of the old one any time they need a new feature?
  - intended 1y ago
    By then the person who suggested the idea has left the firm.
  - motorest 1y ago
    > I'm not so sure that would work well in practice. How would the inexperienced developer know that the code created by the AI was correct? Not a problem. The industry has evolved to tolerate buggy code that barely works. In fact, in some circles that's what's already expected from the baseline. LLMs change nothing in this regard. In fact, they arguably improve upon this problem as it becomes trivial to implement extensive automated test suites. > What if subtle bugs are introduced that the inexperienced developer didn't catch until it went out into production? That's what is happening in the real world without LLMs entering the picture.
    - 1y ago
kypro 1y ago
> Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X), but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. Why would a human review the code in a few years when AI is far better than the average senior developer? Wouldn't that be as stupid as a human reviewing stockfish's moves in Chess?
0xbadcafebee 1y ago
That's not the million dollar question; anyone who's done any kind of AI coding will tell you it's ridiculously faster. I haven't touched JavaScript, CSS & HTML in like a decade. But I got a whole website created with complex UI interactions in 20 minutes - and no frameworks - by just asking ChatGPT to write stuff for me. And that's the crappy, inefficient way of doing this work. Would have taken me a week to figure out all that. If I'd known how to do it already, and I was very good, perhaps it would have taken the same amount of time? But clearly there is a force-multiplier at work here. The million dollar question is, what are the unintended, unpredicted consequences of developing this way? If AI allows me to write code 10x faster, I might end up with 10x more code. Has our ability to review it gotten equally fast? Will the number of bugs multiply? Will there be new classes of bugs? Will we now hire 1 person where we hired 5 before? If that happens, will the 1 person leaving the company become a disaster? How will hiring work (cuz we have such a stellar track record at that...)? Will the changing economics of creating software now make SaaS no longer viable? Or will it make traditional commercial software companies no longer viable? Will the entire global economy change, the way it did with the rise of the first tech industry? Are we seeing a rebirth? We won't know for sure what the consequences are for a while. But there will be consequences.
jstummbillig 1y ago
This is not where AI-assisted coding is going. Where it is going is: The AI will quickly become better at avoiding these types of mistakes than humans ever were (and are ever going to be), because they can and thus will be RL'ed away. What will be left standing longest is providing the vision wrt what the actual problem is, you want to solve.

by paxys 1y ago

dkdcio 1y ago

> The million dollar (perhaps literally) question is – could @kentonv have written this library quicker by himself without any AI help? I *think* the answer to this is clearly no: or at least, given what we can accomplish today with the tools we have now, and that we are still collectively learning how to effectively use this, there's no way it won't be faster (with effective use) in another 3-6 months to fully-code new solutions with AI. I think it requires a lot of work: well-documented, well-structured codebases with fast built-in feedback loops (good linting/unit tests etc.), but we're heading there no

necovek 1y ago
In a "well-documented, well-structured codebase with fast built-in feedback loops", a human programmer is really empowered to make changes fast. This is exactly what's needed for fast iteration, including in unfamiliar codebases. When you are not introducing a new pattern in the code structure, it's mostly copy-paste and then edit. But it's also extremely rare, so a pretty high bar to be able to benefit from tools like AI.
motorest 1y ago
> I think the answer to this is clearly no: or at least, given what we can accomplish today with the tools we have now, and that we are still collectively learning how to effectively use this, there's no way it won't be faster (with effective use) in another 3-6 months to fully-code new solutions with AI. I think these discussions need to start from another point. The techniques changed radically, and so did the way problems are tackled. It's not that a software engineer is/was unable to deliver a project with/without LLMs. That's a red herring. The key aspects are things like the overall quality of the work being delivered vs how much time it took to reach that level of quality. For example, one of the primary ways a LLM is used is not to write code at all: it's to explain to you what you are looking at. Whether it's used as a Google substitute or a rubber duck, developers are able to reason with existing projects and even explore approaches and strategies to tackle problem like they were never able to do so. You no longer need to book meetings with a principal engineer to as questions: you just drop a line in Copilot Chat and ask away. Another critical aspect is that LLMs help you explore options faster, and iterate over them. This allows you to figure out what approach works best for your scenario and adapt to emerging requirements without having to even chat with anyone. This means that, within the timeframe you would deliver the first iteration of a MVP, you can very easily deliver a much more stable project.
- colonCapitalDee 1y ago
  I'm had great success with downloading source code and docs and using Claude Code to query them
- james_marks 1y ago
  Exactly this > Another critical aspect is that LLMs help you explore options faster, and iterate over them. This allows you to figure out what approach works best for your scenario and adapt to emerging requirements without having to even chat with anyone. This means that, within the timeframe you would deliver the first iteration of a MVP, you can very easily deliver a much more stable project

bigstrat2003 1y ago

> but rather experienced engineers using AI to generate bits of code and then meticulously testing and reviewing them. My problem is that (in my experience anyways) this is slower than me just writing the code myself. That's why AI is not a useful tool right now. They only get it right sometimes so it winds up being easier to just do it yourself in the first place. As the saying goes: bad help is worse than no help at all, and AI is bad help right now.

uludag 1y ago
I feel this is on point. So not only is there the time lost correcting and testing AI generated code, but there's also the mental model you build of the code when you write it yourself. Assuming you want a strong mental model of what the code does and how it works (which you'd use in conversations with stakeholders and architecture discussions for example), writing the code manually, with perhaps minor completion-like AI assistance, may be the optimal approach.
JimDabell 1y ago
> My problem is that (in my experience anyways) this is slower than me just writing the code myself. How much experience do you have writing code vs how much experience do you have prompting using AI though? You have to factor in that these tools are new and everybody is still figuring out how to use them effectively.
- imiric 1y ago
  > You have to factor in that these tools are new and everybody is still figuring out how to use them effectively. I think that the skills required are highly overblown. The user should be aware of what each model excels at, its context size, temperature, and other parameters; how to communicate well, set system prompts and phrase tasks in a clear, succinct yet informative way; how to refocus the session when it veers off track; keep up to date with the latest (<~6mo) concepts and tooling, and so on. All of this is trivial for a competent software engineer. The idea that it requires some specialized training that couldn't be attained by experimentation and reading a blog post is absurd. "Prompt engineering" just isn't a thing.

belter 1y ago

The million-dollar question is not whether you can review at the speed the model is coding. It is whether you can trust review alone to catch everything. If a robot assembles cars at lightning speed... but occasionally misaligns a bolt, and your only safeguard is a visual inspection afterward, some defects will roll off the assembly line. Human coders prevent many bugs by thinking during assembly.

chrisweekly 1y ago
THIS. IMHO more rigorous test automation (including fuzzing and related techniques) is needed. Actually that holds whether AI is involved or not, but probably more so if it is.
pton_xd 1y ago
> Human coders prevent many bugs by thinking during assembly. I'm far from an AI true believer but come on -- human coders write bugs, tons and tons of bugs. According to Peopleware, software has "an average defect density of one to three defects per hundred lines of code"!
- belter 1y ago
  My point is that the bugs generated by LLM or human coders are different.
Shorn 1y ago
And yet, doors still fall off airplanes without any AI in sight.

gokhan 1y ago

> Not software engineers being kicked out ... but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. But what if you only need 2 kentonv's instead of 20 at the end? Do you assume we'll find enough new tasks that will occupy the other 18? I think that's the question. And the author is implementing a fairly technical project in this case. How about routine LoB app development?

paxys 1y ago
Increased productivity means increased opportuntity. There isn't going to be a time (at least not anytime soon) when we can all sit back and say "yup, we have accomplished everything there is to do with software and don't need more engineers".
- spiderice 1y ago
  But there very well might be a time very soon where human's no longer offer economic value to the software engineering process. If you could (and currently you can't) pay an AI $10k/year to do what a human could do in a year, why would you pay the human 6 figures? Or even $20k? Nobody is claiming that human's won't have jobs simply because "we have accomplished everything this is to do". It's that humans will offer zero economic value compared to AI because AI gets so good and so cheap.
  - paxys 1y ago
    And there might be a giant asteroid that strikes the earth a few years down the line ending human civilization. If there is some magic $10k AI that can fully replace a $200k software engineer then I'd love to see it. Until that happens this entire discussion is science fiction.
    - spiderice 1y ago
      If experts were saying the astroid will hit earth in the next 5 years, would it still be science fiction? You acting like those two scenarios are the same is disingenuous. Fuck that.

danans 1y ago

> Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X) The theory of enshittification says that "business person pressing a few buttons" approach will be pursued, even if it lowers quality, to save costs, at least until that approach undermines quality so much that it undermines the business model. However, nobody knows how much quality tradeoff tolerance is there to mine.

stackskipton 1y ago

>experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them And where are supposed to get experienced engineers if replaced all Jr Devs with AI? There is a ton of benefit from drudgery of writing classes even if seems like grunt work at the time.

hooverd 1y ago

AI is great for undifferentiated heavy lifting and surfacing knowledge, but by the time I've made all the decisions, I can just write the code that matters myself there.

kentonv 1y ago

It took me a few days to build the library with AI. I estimate it would have taken a few weeks, maybe months to write by hand. That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec. In my attempts to make changes to the Workers Runtime itself using AI, I've generally not felt like it saved much time. Though, people who don't know the codebase as well as I do have reported it helped them a lot. I have found AI incredibly useful when I jump into other people's complex codebases, that I'm not familiar with. I now feel like I'm comfortable doing that, since AI can help me find my way around very quickly, whereas previously I generally shied away from jumping in and would instead try to get someone on the team to make whatever change I needed.

philipwhiuk 1y ago
> Though, people who don't know the codebase as well as I do have reported it helped them a lot. My problem I guess is that maybe this is just Dunning-Kruger esq. When you don't know what you don't know you get the impression it's smart. When you do, you think it's rubbish. Like when you see a media report on a subject you know about and you see it's inaccurate but then somehow still trust the media on a subject you're a non-expert on.
- throwaway314155 1y ago
  I think most of this just amounts to the same old good developers vs. bad developers situation that we've been in for decades.
- giantrobot 1y ago
  > Like when you see a media report on a subject you know about and you see it's inaccurate but then somehow still trust the media on a subject you're a non-expert on. Gell-Mann Amnesia https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect
- motorest 1y ago
  > My problem I guess is that maybe this is just Dunning-Kruger esq. When you don't know what you don't know you get the impression it's smart. When you do, you think it's rubbish. I see your point. Indeed there are two completely different points of view regarding the output of LLMs: * Hey, I managed to vibecode my way into a fully working web service with a React SPA after a couple of prompts, and a full automated test suite to boot. * This project is nowhere as clean as I would have written it, and doesn't even follow my pet coding conventions. One side lauds LLMs, the other complains they output mainly crap. The truth of the matter is that the vast majority of software engineers write crap code, as the definition of "crap code" is "something I would have done differently". Opinionated engineers look at the output of LLMs and accuse it of being crap code. Eppur si muove.

deleted 1y ago

[deleted]

tkiolp4 1y ago

Why is speed important in this context? If the code is published one week/month later, would that affect what exactly? It’s open source.

kentonv 1y ago
As it happens, if this were released a month later, it would have been a huge loss for us. This OAuth library is a core component of the Workers Remote MCP framework, which we managed to ship the day before the Remote MCP standard dropped. And because we were there and ready for customers right at the beginning, a whole lot of people ended up building their MCP servers on us, including some big names: https://blog.cloudflare.com/mcp-demo-day/ https://blog.cloudflare.com/mcp-demo-day/ (Also if I had spent a month on this instead of a few days, that would be a month I wasn't spending on other things, and I have kind of a lot to do...)

motorest 1y ago

> This is exactly the direction I expect AI-assisted coding to go in. Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X), but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. There is a middle ground: software engineers being kicked out because now some business person can hand over the task of building the entire OAuth infrastructure to a single inexperienced developer with a Claude account.

petersellers 1y ago
I'm not so sure that would work well in practice. How would the inexperienced developer know that the code created by the AI was correct? What if subtle bugs are introduced that the inexperienced developer didn't catch until it went out into production? What if the developer didn't even know how to debug those problems correctly? Would they know that the code they are writing is maintainable and extensible, or are they just going to generate a new layer of code on top of the old one any time they need a new feature?
- intended 1y ago
  By then the person who suggested the idea has left the firm.
- motorest 1y ago
  > I'm not so sure that would work well in practice. How would the inexperienced developer know that the code created by the AI was correct? Not a problem. The industry has evolved to tolerate buggy code that barely works. In fact, in some circles that's what's already expected from the baseline. LLMs change nothing in this regard. In fact, they arguably improve upon this problem as it becomes trivial to implement extensive automated test suites. > What if subtle bugs are introduced that the inexperienced developer didn't catch until it went out into production? That's what is happening in the real world without LLMs entering the picture.
  - 1y ago

kypro 1y ago

> Not software engineers being kicked out and some business person pressing a few buttons to have a fully functional app (as is playing out in a lot of fantasies on LinkedIn & X), but rather experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them. Why would a human review the code in a few years when AI is far better than the average senior developer? Wouldn't that be as stupid as a human reviewing stockfish's moves in Chess?

0xbadcafebee 1y ago

That's not the million dollar question; anyone who's done any kind of AI coding will tell you it's ridiculously faster. I haven't touched JavaScript, CSS & HTML in like a decade. But I got a whole website created with complex UI interactions in 20 minutes - and no frameworks - by just asking ChatGPT to write stuff for me. And that's the crappy, inefficient way of doing this work. Would have taken me a week to figure out all that. If I'd known how to do it already, and I was very good, perhaps it would have taken the same amount of time? But clearly there is a force-multiplier at work here. The million dollar question is, what are the unintended, unpredicted consequences of developing this way? If AI allows me to write code 10x faster, I might end up with 10x more code. Has our ability to review it gotten equally fast? Will the number of bugs multiply? Will there be new classes of bugs? Will we now hire 1 person where we hired 5 before? If that happens, will the 1 person leaving the company become a disaster? How will hiring work (cuz we have such a stellar track record at that...)? Will the changing economics of creating software now make SaaS no longer viable? Or will it make traditional commercial software companies no longer viable? Will the entire global economy change, the way it did with the rise of the first tech industry? Are we seeing a rebirth? We won't know for sure what the consequences are for a while. But there will be consequences.

jstummbillig 1y ago

This is not where AI-assisted coding is going. Where it is going is: The AI will quickly become better at avoiding these types of mistakes than humans ever were (and are ever going to be), because they can and thus will be RL'ed away. What will be left standing longest is providing the vision wrt what the actual problem is, you want to solve.